抄録
Since the year of 1977, the world of medical diagnosis has entered a new era with the invention of the modern magnetic resonance imaging (MRI) scanners. These scanners can produce high quality images of any part of the human body. MRI techniques are the method of choice in the diagnosis of tumors, and malformations of the brain because of the incredible ability of distinguishing between the different tissue types, and therefore providing valuable information about the problem at hand. In this thesis a method of segmenting an MRI data set of the brain and providing a 3D model of the white matter, the gray matter and the CSF is introduced. This method consists of three major steps: Skull Stripping, Tissue Classification and Volume rendering. The skull stripping method uses an anisotropic diffusion filter for noise removal, Marr-Helder edge detector to isolate anatomical boundries and a set of morphological functions to remove the skull from the data set. The extraction of different tissue types is then carried out according to the different signals each tissue type produces during the MRI process. This method is based on thresholding, and extracts all the pixels of the desired tissue type that fall within a specified interval. For the volume visualization of the results of the segmentation many procedures exist nowadays. This thesis uses the Visualization Toolkit, which is probably the most used one in the field of medical imaging. VTK provides the user with many libraries that could be used to suit the data type in question. For perfect visualization to be achieved, suitable functions had to be called and appropriate variables had to be set properly. Three accurate 3-D models of white matter, gray matter and cerebrospinal fluid were realized in this thesis according to the method described above.
